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Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques.

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Summary
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This study enhances e-learning using artificial intelligence and multiagent systems. Extra Trees feature selection with machine learning algorithms improved student performance prediction.

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Area of Science:

  • Artificial Intelligence
  • Educational Technology
  • Machine Learning

Background:

  • Artificial Intelligence (AI) is increasingly integrated into various sectors, including education.
  • Multiagent Systems (MAS) are intelligent systems that facilitate e-learning by enabling agent interaction.
  • Feature selection is crucial for enhancing machine learning algorithm performance by identifying relevant data attributes.

Purpose of the Study:

  • To propose an effective multiagent-based system for machine learning and feature selection in e-learning.
  • To enhance the e-learning process by accurately predicting student pass or fail results.
  • To evaluate the impact of feature selection methods on machine learning algorithm performance.

Main Methods:

  • Utilized univariate and Extra Trees feature selection methods to identify essential attributes.
  • Applied five machine learning algorithms: Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors (KNN).
  • Compared algorithm performance using all features versus selected features through cross-validation and testing.

Main Results:

  • The Extra Trees feature selection method, combined with machine learning algorithms, demonstrated superior performance.
  • Selected features significantly improved the predictive accuracy of the applied machine learning models.
  • The study identified optimal feature selection and machine learning combinations for e-learning.

Conclusions:

  • Multiagent systems and feature selection are effective in enhancing AI-driven e-learning environments.
  • Extra Trees feature selection offers a robust approach for improving machine learning model performance in education.
  • The proposed system provides a valuable tool for predicting student outcomes and optimizing the e-learning experience.